articleScientific ReportsApr 28, 2020GOLD OA

BEHRT: Transformer for Electronic Health Records

University of Oxford

PubMed
Indexed incrossrefdoajpubmed

Abstract

Today, despite decades of developments in medicine and the growing interest in precision healthcare, vast majority of diagnoses happen once patients begin to show noticeable signs of illness. Early indication and detection of diseases, however, can provide patients and carers with the chance of early intervention, better disease management, and efficient allocation of healthcare resources. The latest developments in machine learning (including deep learning) provides a great opportunity to address this unmet need. In this study, we introduce BEHRT: A deep neural sequence transduction model for electronic health records (EHR), capable of simultaneously predicting the likelihood of 301 conditions in one's future…

Citation impact

516
total citations
FWCI
32.37
Percentile
100%
References
42
Citations per year

Authors

9

Topics & keywords

Keywords
  • Health records
  • Medical diagnosis
  • Artificial intelligence
  • Computer science
  • Machine learning
  • Deep learning
  • Scalability
  • Electronic health record
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Funding